code: def testCapturedInputs(self):
      a = constant_op.constant(3, dtype=dtypes.int64)
      b = constant_op.constant(4, dtype=dtypes.int64)
      some_tensor = math_ops.mul(a, b)
      function = lambda x: x * x
  
      def predicate(y):
          return math_ops.less(
              math_ops.cast(y, dtypes.int64), some_tensor
          )
  
      # We currently do not support functions with captured inputs.
      dataset = (
          dataset_ops.Dataset.range(10)
          .apply(testing.assert_next(["Map", "Filter"]))
          .map(function)
          .filter(predicate)
      )
      options = options_lib.Options()
      options.experimental_optimization.apply_default_optimizations = (
          False
      )
      options.experimental_optimization.map_and_filter_fusion = True
      dataset = dataset.with_options(options)
      self._testDataset(dataset, function, predicate)
  
file_path: tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/map_and_filter_fusion_test.py
imports: import functools
  from absl.testing import parameterized
  from tensorflow.python.data.experimental.ops import testing
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import options
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 3 for calling the MapAndFilterFusionTest obj's underlying member method code with example usage: obj.testCapturedInputs() and returns: math_ops